






"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						return (preds == labels).mean()




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				str      =  field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,     )




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				str      =  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()						)}						)
					lowercase_    :				str      =  field(metadata={"""help""": """Should contain the data files for the task."""}						)
					lowercase_    :				int      =  field(
					    default=128   ,     metadata={
					        """help""": (
					            """The maximum total input sequence length after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded."""
					        )
					    }   ,     )
					lowercase_    :				bool      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Tuple						=			parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir )
						    and os.listdir(training_args.output_dir )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    ' --overwrite_output_dir to overcome.' )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,      datefmt='%m/%d/%Y %H:%M:%S'     ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,      )
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'     ,      training_args.local_rank     ,      training_args.device     ,      training_args.n_gpu     ,      bool(training_args.local_rank != -1 )     ,      training_args.fpaa     ,      )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('Training/evaluation parameters %s'     ,      a__ )

						# Set seed
						set_seed(training_args.seed )

						try:
												lowerCAmelCase   :Optional[int]						=			processors[data_args.task_name]()
												lowerCAmelCase   :Optional[int]						=			processor.get_labels()
												lowerCAmelCase   :List[str]						=			len(a__ )
						except KeyError:
												raise ValueError('Task not found: %s' % (data_args.task_name) )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCAmelCase   :Tuple						=			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,      num_labels=a__     ,      finetuning_task=data_args.task_name     ,      cache_dir=model_args.cache_dir     ,      )
						lowerCAmelCase   :Optional[int]						=			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      )
						lowerCAmelCase   :int						=			AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path     ,      from_tf=bool('.ckpt' in model_args.model_name_or_path )     ,      config=a__     ,      cache_dir=model_args.cache_dir     ,      )

						# Get datasets
						lowerCAmelCase   :Optional[int]						=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir     ,      tokenizer=a__     ,      task=data_args.task_name     ,      max_seq_length=data_args.max_seq_length     ,      overwrite_cache=data_args.overwrite_cache     ,      mode=Split.train     ,      )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase   :int						=			(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir     ,      tokenizer=a__     ,      task=data_args.task_name     ,      max_seq_length=data_args.max_seq_length     ,      overwrite_cache=data_args.overwrite_cache     ,      mode=Split.dev     ,      )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(a__ ) -> Dict:
												lowerCAmelCase   :List[str]						=			np.argmax(p.predictions     ,      axis=1 )
												return {"acc": simple_accuracy(a__     ,      p.label_ids )}

						# Data collator
						lowerCAmelCase   :List[str]						=			DataCollatorWithPadding(a__     ,      pad_to_multiple_of=8 ) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase   :Any						=			Trainer(
						    model=a__     ,      args=a__     ,      train_dataset=a__     ,      eval_dataset=a__     ,      compute_metrics=a__     ,      data_collator=a__     ,      )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
						lowerCAmelCase   :int						=			{}
						if training_args.do_eval:
												logger.info('*** Evaluate ***' )

												lowerCAmelCase   :Any						=			trainer.evaluate()

												lowerCAmelCase   :Tuple						=			os.path.join(training_args.output_dir     ,      'eval_results.txt' )
												if trainer.is_world_master():
																		with open(a__     ,      'w' ) as writer:
																								logger.info('***** Eval results *****' )
																								for key, value in result.items():
																														logger.info('  %s = %s'     ,      a__     ,      a__ )
																														writer.write('%s = %s\n' % (key, value) )

																								results.update(a__ )

						return results


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()







"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	int   )	->       Tuple:
											if dst_width < 0 or dst_height < 0:
																	raise ValueError('Destination width/height should be > 0'   )

											lowerCAmelCase   :List[Any]						=			img
											lowerCAmelCase   :Optional[int]						=			img.shape[1]
											lowerCAmelCase   :Union[str, Any]						=			img.shape[0]
											lowerCAmelCase   :Optional[int]						=			dst_width
											lowerCAmelCase   :Optional[int]						=			dst_height

											lowerCAmelCase   :Any						=			self.src_w / self.dst_w
											lowerCAmelCase   :Dict						=			self.src_h / self.dst_h

											lowerCAmelCase   :int						=			(
											    np.ones((self.dst_h, self.dst_w, 3)     , np.uinta   ) * 255
											)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																							lowerCAmelCase   :Optional[Any]						=			self.img[self.get_y(UpperCAmelCase   )][self.get_x(UpperCAmelCase   )]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_x * x   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_y * y   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   8_00, 6_00
		__SCREAMING_SNAKE_CASE   =   imread('image_data/lena.jpg', 1)
		__SCREAMING_SNAKE_CASE   =   NearestNeighbour(im, dst_w, dst_h)
		n.process()

		imshow(
		    F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
		)
		waitKey(0)
		destroyAllWindows()